BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21057@www.clsp.jhu.edu DTSTAMP:20240329T011653Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab. \nBiography\nHeidi is a Senior Lecturer (associate professor) in Computer Science at the University of Sheffield\, United Kingdom. Her research inte rests are on the application of AI-based voice technologies to healthcare. In particular\, the detection and monitoring of people’s physical and men tal health including verbal and non-verbal traits for expressions of emoti on\, anxiety\, depression and neurodegenerative conditions in e.g.\, thera peutic or diagnostic settings. DTSTART;TZID=America/New_York:20211119T120000 DTEND;TZID=America/New_York:20211119T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Heidi Christensen (University of Sheffield\, UK) Virtual Seminar “A utomated Processing of Pathological Speech: Recent Work and Ongoing Challe nges” URL:https://www.clsp.jhu.edu/events/heidi-christensen-university-of-sheffie ld-uk-virtual-seminar-automated-processing-of-pathological-speech-recent-w ork-and-ongoing-challenges/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab.
\nBiography
\nHeidi is a Senior Lecturer (as sociate professor) in Computer Science at the University of Sheffield\, Un ited Kingdom. Her research interests are on the application of AI-based vo ice technologies to healthcare. In particular\, the detection and monitori ng of people’s physical and mental health including verbal and non-verbal traits for expressions of emotion\, anxiety\, depression and neurodegenera tive conditions in e.g.\, therapeutic or diagnostic settings.
\n X-TAGS;LANGUAGE=en-US:2021\,Christensen\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-22374@www.clsp.jhu.edu DTSTAMP:20240329T011653Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nIn recent years\, the field of Natural Language Proce ssing has seen a profusion of tasks\, datasets\, and systems that facilita te reasoning about real-world situations through language (e.g.\, RTE\, MN LI\, COMET). Such systems might\, for example\, be trained to consider a s ituation where “somebody dropped a glass on the floor\,” and conclude it i s likely that “the glass shattered” as a result. In this talk\, I will dis cuss three pieces of work that revisit assumptions made by or about these systems. In the first work\, I develop a Defeasible Inference task\, which enables a system to recognize when a prior assumption it has made may no longer be true in light of new evidence it receives. The second work I wil l discuss revisits partial-input baselines\, which have highlighted issues of spurious correlations in natural language reasoning datasets and led t o unfavorable assumptions about models’ reasoning abilities. In particular \, I will discuss experiments that show models may still learn to reason i n the presence of spurious dataset artifacts. Finally\, I will touch on wo rk analyzing harmful assumptions made by reasoning models in the form of s ocial stereotypes\, particularly in the case of free-form generative reaso ning models.\nBiography\nRachel Rudinger is an Assistant Professor in the Department of Computer Science at the University of Maryland\, College Par k. She holds joint appointments in the Department of Linguistics and the I nstitute for Advanced Computer Studies (UMIACS). In 2019\, Rachel complete d her Ph.D. in Computer Science at Johns Hopkins University in the Center for Language and Speech Processing. From 2019-2020\, she was a Young Inves tigator at the Allen Institute for AI in Seattle\, and a visiting research er at the University of Washington. Her research interests include computa tional semantics\, common-sense reasoning\, and issues of social bias and fairness in NLP. DTSTART;TZID=America/New_York:20220916T120000 DTEND;TZID=America/New_York:20220916T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rachel Rudinger (University of Maryland\, College Park) “Not So Fas t!: Revisiting Assumptions in (and about) Natural Language Reasoning” URL:https://www.clsp.jhu.edu/events/rachel-rudinger-university-of-maryland- college-park-not-so-fast-revisiting-assumptions-in-and-about-natural-langu age-reasoning/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nIn recent years\, the field of Natural Language Proce ssing has seen a profusion of tasks\, datasets\, and systems that facilita te reasoning about real-world situations through language (e.g.\, RTE\, MN LI\, COMET). Such systems might\, for example\, be trained to consider a s ituation where “somebody dropped a glass on the floor\,” and conclude it i s likely that “the glass shattered” as a result. In this talk\, I will dis cuss three pieces of work that revisit assumptions made by or about these systems. In the first work\, I develop a Defeasible Inference task\, which enables a system to recognize when a prior assumption it has made may no longer be true in light of new evidence it receives. The second work I wil l discuss revisits partial-input baselines\, which have highlighted issues of spurious correlations in natural language reasoning datasets and led t o unfavorable assumptions about models’ reasoning abilities. In particular \, I will discuss experiments that show models may still learn to reason i n the presence of spurious dataset artifacts. Finally\, I will touch on wo rk analyzing harmful assumptions made by reasoning models in the form of s ocial stereotypes\, particularly in the case of free-form generative reaso ning models.
\nBiography
\nRachel Rudinger is an Assistant Professor in the Department of Computer Science at the Unive rsity of Maryland\, College Park. She holds joint appointments in the Depa rtment of Linguistics and the Institute for Advanced Computer Studies (UMI ACS). In 2019\, Rachel completed her Ph.D. in Computer Science at Johns Ho pkins University in the Center for Language and Speech Processing. From 20 19-2020\, she was a Young Investigator at the Allen Institute for AI in Se attle\, and a visiting researcher at the University of Washington. Her res earch interests include computational semantics\, common-sense reasoning\, and issues of social bias and fairness in NLP.
\n X-TAGS;LANGUAGE=en-US:2022\,Rudinger\,September END:VEVENT END:VCALENDAR